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dc.rights.licenseopenen_US
dc.contributor.authorSANSA, Ines
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorBOUSSAADA, Zina
dc.contributor.authorBELLAAJ, Najiba
dc.date.accessioned2023-04-05T08:14:11Z
dc.date.available2023-04-05T08:14:11Z
dc.date.issued2021-11
dc.identifier.issn1996-1073en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/172754
dc.description.abstractEnThe prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. The interest in solar radiation prediction is increasing nowadays so efficient prediction can greatly improve the performance of these different applications. This paper presents a novel solar radiation prediction approach which combines two models, the Auto Regressive Moving Average (ARMA) and the Nonlinear Auto Regressive with eXogenous input (NARX). This choice has been carried out in order to take the advantages of both models to produce better prediction results. The performance of the proposed hybrid model has been validated using a real database corresponding to a company located in Barcelona north. Simulation results have proven the effectiveness of this hybrid model to predict the weekly solar radiation averages. The ARMA model is suitable for small variations of solar radiation while the NARX model is appropriate for large solar radiation fluctuations.
dc.language.isoENen_US
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enPV power
dc.subject.enprediction
dc.subject.enARMA
dc.subject.enNARX
dc.subject.enhybrid model
dc.title.enSolar Radiation Prediction Using a Novel Hybrid Model of ARMA and NARX
dc.typeArticle de revueen_US
dc.identifier.doi10.3390/en14216920en_US
dc.subject.halSciences de l'ingénieur [physics]/Autreen_US
bordeaux.journalEnergiesen_US
bordeaux.page6920en_US
bordeaux.volume14en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.issue21en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionBordeaux Sciences Agroen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-03481404
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Energies&rft.date=2021-11&rft.volume=14&rft.issue=21&rft.spage=6920&rft.epage=6920&rft.eissn=1996-1073&rft.issn=1996-1073&rft.au=SANSA,%20Ines&BOUSSAADA,%20Zina&BELLAAJ,%20Najiba&rft.genre=article


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